ITR: Adaptive Query Processing Architecture for Streaming Geospatial Image Data

University Of California-Davis, Davis CA

Investigators

Abstract

This research project will develop models, techniques, and architectures for the adaptive processing of real-time remotely sensed, streaming geospatial satellite image data. It will develop a generic stream format, called GeoStreams, for remotely sensed, geospatial image data, based on the widely used image data formats to process continuous queries in an adaptive fashion and to deliver tailored geospatial image data to consumers. The GeoStream query model and query processing framework will extend current models and techniques for processing continuous queries on data streams to handle complex query frameworks on geospatial data. The main focus will be on query processing techniques to optimize multi-query processing of spatial and sensor band selections. Additional important operators on GeoStreams include sensor band algebra, pixel neighborhood processing, spatial filtering, aggregation, and spatial reference transformations. National Oceanic and Atmospheric Administration's (NOAA) Geostationary Operational Environmental Satellite (GOES) data, directly accessible in this project, will serve as a primary testbed for the research. A major testbed application will be in support of water balance applications. The goal is to calculate spatially distributed daily reference evapotranspiration maps for the State of California at high spatial resolution (1 km2-16 km2), and in real-time. The methodology will use GOES data, properly combined with weather station data, and ancillary spatial data such as digital elevation maps.